论文标题
使用基于图的方法的手势识别的文献
Literature on Hand GESTURE Recognition using Graph based methods
论文作者
论文摘要
基于骨架的识别系统正在获得流行和机器学习模型，重点是骨骼中的点或关节，已被证明在机器人技术等许多领域具有计算有效和应用。很容易跟踪点，从而保留空间和时间信息，这在抽象所需信息中起着重要作用，分类成为一项容易的任务。在本文中，我们旨在研究这些观点，但使用云机制，将云定义为要点的集合。但是，当我们添加时间信息时，可能不可能检索每个帧中一个点的坐标，而不是专注于单个点，我们可以使用k-neighbors来检索所讨论的观点的状态。我们的重点是使用重量共享收集此类信息，但请确保当我们尝试从邻居那里检索信息时，我们不会随身携带噪音。 LSTM具有长期建模功能，并且可以携带时间和空间信息。在本文中，我们试图总结基于图形的手势识别方法。
Skeleton based recognition systems are gaining popularity and machine learning models focusing on points or joints in a skeleton have proved to be computationally effective and application in many areas like Robotics. It is easy to track points and thereby preserving spatial and temporal information, which plays an important role in abstracting the required information, classification becomes an easy task. In this paper, we aim to study these points but using a cloud mechanism, where we define a cloud as collection of points. However, when we add temporal information, it may not be possible to retrieve the coordinates of a point in each frame and hence instead of focusing on a single point, we can use k-neighbors to retrieve the state of the point under discussion. Our focus is to gather such information using weight sharing but making sure that when we try to retrieve the information from neighbors, we do not carry noise with it. LSTM which has capability of long-term modelling and can carry both temporal and spatial information. In this article we tried to summarise graph based gesture recognition method.
